> ## Documentation Index
> Fetch the complete documentation index at: https://docs.prefect.io/llms.txt
> Use this file to discover all available pages before exploring further.

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  ## Submitting Feedback
  If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback via POST to:
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# ml_datastore

# `prefect_azure.ml_datastore`

Tasks for interacting with Azure ML Datastore

## Functions

### `ml_list_datastores` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/integrations/prefect-azure/prefect_azure/ml_datastore.py#L25" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python  theme={null}
ml_list_datastores(ml_credentials: 'AzureMlCredentials') -> Dict
```

Lists the Datastores in the Workspace.

**Args:**

* `ml_credentials`: Credentials to use for authentication with Azure.

### `ml_get_datastore` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/integrations/prefect-azure/prefect_azure/ml_datastore.py#L79" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python  theme={null}
ml_get_datastore(ml_credentials: 'AzureMlCredentials', datastore_name: Optional[str] = None) -> Datastore
```

Gets the Datastore within the Workspace.

**Args:**

* `ml_credentials`: Credentials to use for authentication with Azure.
* `datastore_name`: The name of the Datastore. If `None`, then the
  default Datastore of the Workspace is returned.

### `ml_upload_datastore` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/integrations/prefect-azure/prefect_azure/ml_datastore.py#L119" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python  theme={null}
ml_upload_datastore(path: Union[str, Path, List[Union[str, Path]]], ml_credentials: 'AzureMlCredentials', target_path: Union[str, Path, None] = None, relative_root: Union[str, Path, None] = None, datastore_name: Optional[str] = None, overwrite: bool = False) -> 'DataReference'
```

Uploads local files to a Datastore.

**Args:**

* `path`: The path to a single file, single directory,
  or a list of path to files to be uploaded.
* `ml_credentials`: Credentials to use for authentication with Azure.
* `target_path`: The location in the blob container to upload to. If
  None, then upload to root.
* `relative_root`: The root from which is used to determine the path of
  the files in the blob. For example, if we upload /path/to/file.txt,
  and we define base path to be /path, when file.txt is uploaded
  to the blob storage, it will have the path of /to/file.txt.
* `datastore_name`: The name of the Datastore. If `None`, then the
  default Datastore of the Workspace is returned.
* `overwrite`: Overwrite existing file(s).

### `ml_register_datastore_blob_container` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/integrations/prefect-azure/prefect_azure/ml_datastore.py#L208" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python  theme={null}
ml_register_datastore_blob_container(container_name: str, ml_credentials: 'AzureMlCredentials', blob_storage_credentials: 'AzureBlobStorageCredentials', datastore_name: Optional[str] = None, create_container_if_not_exists: bool = False, overwrite: bool = False, set_as_default: bool = False) -> 'AzureBlobDatastore'
```

Registers a Azure Blob Storage container as a
Datastore in a Azure ML service Workspace.

**Args:**

* `container_name`: The name of the container.
* `ml_credentials`: Credentials to use for authentication with Azure ML.
* `blob_storage_credentials`: Credentials to use for authentication
  with Azure Blob Storage.
* `datastore_name`: The name of the datastore. If not defined, the
  container name will be used.
* `create_container_if_not_exists`: Create a container, if one does not
  exist with the given name.
* `overwrite`: Overwrite an existing datastore. If
  the datastore does not exist, it will be created.
* `set_as_default`: Set the created Datastore as the default datastore
  for the Workspace.


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